Computer-Implemented System And Method For Identifying Relevant Documents

ABSTRACT

A computer-implemented system and method for identifying relevant documents is provided. A set of documents each associated with one or more concepts is identified. At least a portion of the documents in the set are clustered based on the concepts. A matrix that provides a summary of the documents most relevant to one such concept is generated by determining for each document a measure of similarity between a concept frequency occurrence and concept weights of each cluster. The matrix is populated with the calculated measures of similarity. Those documents associated with a threshold measure of similarity are identified as the most relevant documents to one such concept.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent application is a continuation of U.S. patent applicationSer. No. 14/949,829, filed Nov. 23, 2015, pending; which is acontinuation of U.S. Pat. No. 9,195,399, issued Nov. 24, 2015; which isa continuation of U.S. Pat. No. 8,725,736, issued May 13, 2014; which isa continuation of U.S. Pat. No. 8,380,718, issued Feb. 19, 2013; whichis a continuation of U.S. Pat. No. 8,015,188, issued Sep. 6, 2011; whichis a continuation of U.S. Pat. No. 7,809,727, issued Oct. 5, 2010; whichis a continuation of U.S. Pat. No. 7,313,556, issued Dec. 25, 2007;which is a continuation of U.S. Pat. No. 6,978,274, issued Dec. 20,2005, the priority filing dates of which are claimed and the disclosuresof which are incorporated by reference.

FIELD

The present invention relates in general to text mining and, inparticular, to a computer-implemented system and method for identifyingrelevant documents.

BACKGROUND

Document warehousing extends data warehousing to content mining andretrieval. Document warehousing attempts to extract semantic informationfrom collections of unstructured documents to provide conceptualinformation with a high degree of precision and recall. Documents in adocument warehouse share several properties. First, the documents lack acommon structure or shared type. Second, semantically-related documentsare integrated through text mining. Third, essential document featuresare extracted and explicitly stored as part of the document warehouse.Finally, documents are often retrieved from multiple and disparatesources, such as over the Internet or as electronic messages.

Document warehouses are built in stages to deal with a wide range ofinformation sources. First, document sources are identified anddocuments are retrieved into a repository. For example, the documentsources could be electronic messaging folders or Web content retrievedover the Internet. Once retrieved, the documents are pre-processed toformat and regularize the information into a consistent manner. Next,during text analysis, text mining is performed to extract semanticcontent, including identifying dominant themes, extracting key featuresand summarizing the content. Finally, metadata is compiled from thesemantic context to explicate essential attributes. Preferably, themetadata is provided in a format amenable to normalized queries, such asdatabase management tools. Document warehousing is described in D.Sullivan, “Document Warehousing and Text Mining, Techniques forImproving Business Operations, Marketing, and Sales,” Chs. 1-3, WileyComputer Publishing (2001), the disclosure of which is incorporated byreference.

Text mining is at the core of the data warehousing process. Text mininginvolves the compiling, organizing and analyzing of document collectionsto support the delivery of targeted types of information and to discoverrelationships between relevant facts. However, identifying relevantcontent can be difficult. First, extracting relevant content requires ahigh degree of precision and recall. Precision is the measure of howwell the documents returned in response to a query actually address thequery criteria. Recall is the measure of what should have been returnedby the query. Typically, the broader and less structured the documents,the lower the degree of precision and recall. Second, analyzing anunstructured document collection without the benefit of a prioriknowledge in the form of keywords and indices can present a potentiallyintractable problem space. Finally, synonymy and polysemy can cloud andconfuse extracted content. Synonymy refers to multiple words having thesame meaning and polysemy refers to a single word with multiplemeanings. Fine-grained text mining must reconcile synonymy and polysemyto yield meaningful results.

In the prior art, text mining is performed in two ways. First, syntacticsearching provides a brute force approach to analyzing and extractingcontent based on literal textual attributes found in each document.Syntactic searching includes keyword and proximate keyword searching aswell as rule-based searching through Boolean relationships. Syntacticsearching relies on predefined indices of keywords and stop words tolocate relevant information. However, there are several ways to expressany given concept. Accordingly, syntactic searching can fail to yieldsatisfactory results due to incomplete indices and poorly structuredsearch criteria.

A more advanced prior art approach uses a vector space model to searchfor underlying meanings in a document collection. The vector space modelemploys a geometric representation of documents using word vectors.Individual keywords are mapped into vectors in multi-dimensional spacealong axes representative of query search terms. Significant terms areassigned a relative weight and semantic content is extracted based onthreshold filters. Although substantially overcoming the shortcomings ofsyntactic searching, the multivariant and multidimensional nature of thevector space model can lead to a computationally intractable problemspace. As well, the vector space model fails to resolve the problems ofsynonymy and polysemy.

Therefore, there is a need for an approach to dynamically evaluatingconcepts inherent in a collection of documents. Such an approach wouldpreferably dynamically discover the latent meanings without the use of apriori knowledge or indices. Rather, the approach would discoversemantic relationships between individual terms given the presence ofanother item.

There is a further need for an approach to providing a graphicalvisualization of concepts extracted from a document set through semanticindexing. Preferably, such an approach would extract the underlyingmeanings of documents through statistics and linear algebraic techniquesto find clusters of terms and phrases representative of the concepts.

SUMMARY

The present invention provides a system and method for indexing andevaluating unstructured documents through analysis of dynamicallyextracted concepts. A set of unstructured documents is identified andretrieved into a document warehouse repository. Individual concepts areextracted from the documents and mapped as normalized data into adatabase. The frequencies of occurrence of each concept within eachdocument and over all documents are determined and mapped. A corpusgraph is generated to display a minimized set of concepts whereby eachconcept references at least two documents and no document in the corpusis unreferenced. A subset of documents occurring within predefined edgeconditions of a median value are selected. Clusters of concepts aregrouped into themes. Inner products of document concept frequencyoccurrences and cluster concept weightings are mapped into amulti-dimensional concept space for each theme and iteratively generateduntil the clusters settle. The resultant data minima indicates thosedocuments having the most pertinence to the identified concepts.

An embodiment provides a computer-implemented system and method foridentifying relevant documents. A set of documents each associated withone or more concepts is identified. At least a portion of the documentsin the set are clustered based on the concepts. A matrix that provides asummary of the documents most relevant to one such concept is generatedby determining for each document a measure of similarity between aconcept frequency occurrence and concept weights of each cluster. Thematrix is populated with the calculated measures of similarity. Thosedocuments associated with a threshold measure of similarity areidentified as the most relevant documents to one such concept.

In summary, the present invention semantically evaluates terms andphrases with the goal of creating meaningful themes. Documentfrequencies and co-occurrences of terms and phrases are used to select aminimal set of highly correlated terms and phrases that reference alldocuments in a corpus.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a system for dynamically evaluatinglatent concepts in unstructured documents, in accordance with thepresent invention.

FIG. 2 is a block diagram showing the software modules implementing thedocument analyzer of FIG. 1.

FIG. 3 is a process flow diagram showing the stages of text analysisperformed by the document analyzer of FIG. 1.

FIG. 4 is a flow diagram showing a method for dynamically evaluatinglatent concepts in unstructured documents, in accordance with thepresent invention.

FIG. 5 is a flow diagram showing the routine for performing textanalysis for use in the method of FIG. 4.

FIG. 6 is a flow diagram showing the routine for creating a histogramfor use in the routine of FIG. 5.

FIG. 7 is a data structure diagram showing a database record for aconcept stored in the database 30 of FIG. 1.

FIG. 8 is a data structure diagram showing, by way of example, adatabase table containing a lexicon of extracted concepts stored in thedatabase 30 of FIG. 1.

FIG. 9 is a graph showing, by way of example, a histogram of thefrequencies of concept occurrences generated by the routine of FIG. 6.

FIG. 10 is a table showing, by way of example, concept occurrencefrequencies generated by the routine of FIG. 6.

FIG. 11 is a graph showing, by way of example, a corpus graph of thefrequency of concept occurrences generated by the routine of FIG. 5.

FIG. 12 is a flow diagram showing a routine for creating a matrix foruse in the routine of FIG. 5.

FIG. 13 is a table showing, by way of example, the matrix of themesgenerated by the routine of FIG. 12.

FIG. 14 is a flow diagram showing a routine for determining results foruse in the routine of FIG. 5.

DETAILED DESCRIPTION Glossary

-   -   Keyword: A literal search term which is either present or absent        from a document. Keywords are not used in the evaluation of        documents as described herein.    -   Term: A root stem of a single word appearing in the body of at        least one document.    -   Phrase: Two or more words co-occurring in the body of a        document. A phrase can include stop words.    -   Concept: A collection of terms or phrases with common semantic        meanings.    -   Theme: Two or more concepts with a common semantic meaning.    -   Cluster: All documents for a given concept or theme.        The foregoing terms are used throughout this document and,        unless indicated otherwise, are assigned the meanings presented        above.

FIG. 1 is a block diagram showing a system 11 for dynamically evaluatinglatent concepts in unstructured documents, in accordance with thepresent invention. By way of illustration, the system 11 operates in adistributed computing environment 10 which includes a plurality ofheterogeneous systems and document sources. The system 11 implements adocument analyzer 12, as further described below beginning withreference to FIG. 2, for evaluating latent concepts in unstructureddocuments. The system 11 is coupled to a storage device 13 which storesa document warehouse 14 for maintaining a repository of documents and adatabase 30 for maintaining document information.

The document analyzer 12 analyzes documents retrieved from a pluralityof local sources. The local sources include documents 17 maintained in astorage device 16 coupled to a local server 15 and documents 20maintained in a storage device 19 coupled to a local client 18. Thelocal server 15 and local client 18 are interconnected to the system 11over an intranetwork 21. In addition, the document analyzer 12 canidentify and retrieve documents from remote sources over an internetwork22, including the Internet, through a gateway 23 interfaced to theintranetwork 21. The remote sources include documents 26 maintained in astorage device 25 coupled to a remote server 24 and documents 29maintained in a storage device 28 coupled to a remote client 27.

The individual documents 17, 20, 26, 29 include all forms and types ofunstructured data, including electronic message stores, such aselectronic mail (email) folders, word processing documents or Hypertextdocuments, and could also include graphical or multimedia data.Notwithstanding, the documents could be in the form of structured data,such as stored in a spreadsheet or database. Content mined from thesetypes of documents does not require preprocessing, as described below.

In the described embodiment, the individual documents 17, 20, 26, 29include electronic message folders, such as maintained by the Outlookand Outlook Express products, licensed by Microsoft Corporation,Redmond, Wash. The database is an SQL-based relational database, such asthe Oracle database management system, release 8, licensed by OracleCorporation, Redwood Shores, Calif.

The individual computer systems, including system 11, server 15, client18, remote server 24 and remote client 27, are general purpose,programmed digital computing devices consisting of a central processingunit (CPU), random access memory (RAM), non-volatile secondary storage,such as a hard drive or CD ROM drive, network interfaces, and peripheraldevices, including user interfacing means, such as a keyboard anddisplay. Program code, including software programs, and data are loadedinto the RAM for execution and processing by the CPU and results aregenerated for display, output, transmittal, or storage.

FIG. 2 is a block diagram showing the software modules 40 implementingthe document analyzer 12 of FIG. 1. The document analyzer 12 includesthree modules: storage and retrieval manager 41, text analyzer 42, anddisplay and visualization 43. The storage and retrieval manager 41identifies and retrieves documents 44 into the document warehouse 14(shown in FIG. 1). The documents 44 are retrieved from various sources,including both local and remote clients and server stores. The textanalyzer 42 performs the bulk of the text mining processing. The displayand visualization 43 complements the operations performed by the textanalyzer 42 by presenting visual representations of the informationextracted from the documents 44. The display and visualization 43 canalso generate a graphical representation which preserves independentvariable relationships, such as described in common-assigned U.S. Pat.No. 6,888,548, issued May 3, 2005, the disclosure of which isincorporated by reference.

During text analysis, the text analyzer 42 identifies terms and phrasesand extracts concepts in the form of noun phrases that are stored in alexicon 18 maintained in the database 30. After normalizing theextracted concepts, the text analyzer 42 generates a frequency table 46of concept occurrences, as further described below with reference toFIG. 6, and a matrix 47 of summations of the products of pair-wiseterms, as further described below with reference to FIG. 10. Similarly,the display and visualization 43 generates a histogram 47 of conceptoccurrences per document, as further described below with reference toFIG. 6, and a corpus graph 48 of concept occurrences over all documents,as further described below with reference to FIG. 8.

Each module is a computer program, procedure or module written as sourcecode in a conventional programming language, such as the C++ programminglanguage, and is presented for execution by the CPU as object or bytecode, as is known in the art. The various implementations of the sourcecode and object and byte codes can be held on a computer-readablestorage medium or embodied on a transmission medium in a carrier wave.The document analyzer 12 operates in accordance with a sequence ofprocess steps, as further described below with reference to FIG. 5.

FIG. 3 is a process flow diagram showing the stages 60 of text analysisperformed by the document analyzer 12 of FIG. 1. The individualdocuments 44 are preprocessed and noun phrases are extracted as concepts(transition 61) into a lexicon 45. The noun phrases are normalized andqueried (transition 62) to generate a frequency table 46. The frequencytable 46 identifies individual concepts and their respective frequencyof occurrence within each document 44. The frequencies of conceptoccurrences are visualized (transition 63) into a frequency of conceptshistogram 48. The histogram 48 graphically displays the frequencies ofoccurrence of each concept on a per-document basis. Next, thefrequencies of concept occurrences for all the documents 44 areassimilated (transition 64) into a corpus graph 49 that displays theoverall counts of documents containing each of the extracted concepts.Finally, the most relevant concepts are summarized (transition 65) intoa matrix 46 that presents the results as summations of the products ofpair-wise terms.

FIG. 4 is a flow diagram showing a method 70 for dynamically evaluatinglatent concepts in unstructured documents 44 (shown in FIG. 2), inaccordance with the present invention. As a preliminary step, the set ofdocuments 44 to be analyzed is identified (block 71) and retrieved intothe document warehouse 14 (shown in FIG. 1) (block 72). The documents 44are unstructured data and lack a common format or shared type. Thedocuments 44 include electronic messages stored in messaging folders,word processing documents, hypertext documents, and the like.

Once identified and retrieved, the set of documents 44 is analyzed(block 73), as further described below with reference to FIG. 5. Duringtext analysis, a matrix 47 (shown in FIG. 2) of term-documentassociation data is constructed to summarize the semantic contentinherent in the structure of the documents 44. As well, the frequency ofindividual terms or phrases extracted from the documents 44 aredisplayed and the results are optionally visualized (block 74). Theroutine then terminates.

FIG. 5 is a flow diagram showing the routine 80 for performing textanalysis for use in the method 70 of FIG. 4. The purpose of this routineis to extract and index terms or phrases for the set of documents 44(shown in FIG. 2). Preliminarily, each document in the documents set 44is preprocessed (block 81) to remove stop words. These include commonlyoccurring words, such as indefinite articles (“a” and “an”), definitearticles (“the”), pronouns (“I”, “he” and “she”), connectors (“and” and“or”), and similar non-substantive words.

Following preprocessing, a histogram 48 of the frequency of terms (shownin FIG. 2) is logically created for each document 44 (block 82), asfurther described below with reference to FIG. 6. Each histogram 48, asfurther described below with reference to FIG. 9, maps the relativefrequency of occurrence of each extracted term on a per-document basis.

Next, a document reference frequency (corpus) graph 49, as furtherdescribed below with reference to FIG. 10, is created for all documents44 (block 83). The corpus graph 49 graphically maps thesemantically-related concepts for the entire documents set 44 based onterms and phrases. A subset of the corpus is selected by removing thoseterms and phrases falling outside either edge of predefined thresholds(block 84). For shorter documents, such as email, having lesssemantically-rich content, the thresholds are set from about 1% to about15%, inclusive. Larger documents may require tighter threshold values.

The selected set of terms and phrases falling within the thresholds areused to generate themes (and concepts) (block 85) based on correlationsbetween normalized terms and phrases in the documents set. In thedescribed embodiment, themes are primarily used, rather than individualconcepts, as a single co-occurrence of terms or phrases carries lesssemantic meaning than multiple co-occurrences. As used herein, anyreference to a “theme” or “concept” will be understood to include theother term, except as specifically indicated otherwise.

Next, clusters are created (block 86) from groups of highly-correlatedconcepts and themes. Individual concepts and themes are categorizedbased on, for example, Euclidean distances calculated between each pairof concepts and themes and defined within a pre-specified range ofvariance, such as described in commonly-assigned U.S. Pat. No.6,778,995, issued Aug. 17, 2004, the disclosure of which is incorporatedby reference.

A matrix 47 of the documents 44 is created (block 87), as furtherdescribed below with reference to FIG. 13. The matrix 47 contains theinner products of document concept frequency occurrences and clusterconcept weightings mapped into a multi-dimensional concept space foreach theme. Finally, the results of the text analysis operations aredetermined (block 88), as further described below with reference to FIG.14, after which the routine returns.

FIG. 6 is a flow diagram showing the routine 90 for creating a histogram48 (shown in FIG. 2) for use in the routine of FIG. 5. The purpose ofthis routine is to extract noun phrases representing individual conceptsand to create a normalized representation of the occurrences of theconcepts on a per-document basis. The histogram represents the logicalunion of the terms and phrases extracted from each document. In thedescribed embodiment, the histogram 48 need not be expressly visualized,but is generated internally as part of the text analysis process.

Initially, noun phrases are extracted (block 91) from each document 44.In the described embodiment, concepts are defined on the basis of theextracted noun phrases, although individual nouns or tri-grams (wordtriples) could be used in lieu of noun phrases. In the describedembodiment, the noun phrases are extracted using the LinguistX productlicensed by Inxight Software, Inc., Santa Clara, Calif.

Once extracted, the individual terms or phrases are loaded into recordsstored in the database 30 (shown in FIG. 1) (block 92). The terms storedin the database 30 are normalized (block 93) such that each conceptappears as a record only once. In the described embodiment, the recordsare normalized into third normal form, although other normalizationschemas could be used.

FIG. 7 is a data structure diagram showing a database record 100 for aconcept stored in the database 30 of FIG. 1. Each database record 100includes fields for storing an identifier 101, string 102 and frequency103. The identifier 101 is a monotonically increasing integer value thatuniquely identifies each term or phrase stored as the string 102 in eachrecord 100. The frequency of occurrence of each term or phrase istallied in the frequency 103.

FIG. 8 is a data structure diagram showing, by way of example, adatabase table 110 containing a lexicon 111 of extracted concepts storedin the database 30 of FIG. 1. The lexicon 111 maps out the individualoccurrences of identified terms 113 extracted for any given document112. By way of example, the document 112 includes three terms numbered1, 3 and 5. Concept 1 occurs once in document 112, concept 3 occurstwice, and concept 5 occurs once. The lexicon tallies and represents theoccurrences of frequency of the concepts 1, 3 and 5 across all documents44.

Referring back to FIG. 6, a frequency table is created from the lexicon111 for each given document 44 (block 94). The frequency table is sortedin order of decreasing frequencies of occurrence for each concept 113found in a given document 44. In the described embodiment, all terms andphrases occurring just once in a given document are removed as notrelevant to semantic content. The frequency table is then used togenerate a histogram 48 (shown in FIG. 2) (block 95) which visualizesthe frequencies of occurrence of extracted concepts in each document.The routine then returns.

FIG. 9 is a graph showing, by way of example, a histogram 48 of thefrequencies of concept occurrences generated by the routine of FIG. 6.The x-axis defines the individual concepts 121 for each document and they-axis defines the frequencies of occurrence of each concept 122. Theconcepts are mapped in order of decreasing frequency 123 to generate acurve 124 representing the semantic content of the document 44.Accordingly, terms or phrases appearing on the increasing end of thecurve 124 have a high frequency of occurrence while concepts appearingon the descending end of the curve 124 have a low frequency ofoccurrence.

FIG. 10 is a table 130 showing, by way of example, concept occurrencefrequencies generated by the routine of FIG. 6. Each concept 131 ismapped against the total frequency occurrence 132 for the entire set ofdocuments 44. Thus, for each of the concepts 133, a cumulative frequency134 is tallied. The corpus table 130 is used to generate the documentconcept frequency reference (corpus) graph 49.

FIG. 11 is a graph 140 showing, by way of example, a corpus graph of thefrequency of concept occurrences generated by the routine of FIG. 5. Thegraph 140 visualizes the extracted concepts as tallied in the corpustable 130 (shown in FIG. 10). The x-axis defines the individual concepts141 for all documents and the y-axis defines the number of documents 44referencing each concept 142. The individual concepts are mapped inorder of descending frequency of occurrence 143 to generate a curve 144representing the latent semantics of the set of documents 44.

A median value 145 is selected and edge conditions 146 a-b areestablished to discriminate between concepts which occur too frequentlyversus concepts which occur too infrequently. Those documents fallingwithin the edge conditions 146 a-b form a subset of documents containinglatent concepts. In the described embodiment, the median value 145 isdocument-type dependent. For efficiency, the upper edge condition 146 bis set to 70% and the 64 concepts immediately preceding the upper edgecondition 146 b are selected, although other forms of thresholddiscrimination could also be used.

FIG. 12 is a flow diagram showing the routine 150 for creating a matrix47 (shown in FIG. 2) for use in the routine of FIG. 5. Initially, thosedocuments 44 having zero values for frequency counts are removed throughfiltering (block 151). The inner products of document concept frequencyoccurrences and cluster concept weightings mapped into amulti-dimensional concept space for each theme are calculated and usedto populate the matrix (block 152). The individual cluster weightingsare iteratively updated (block 153) to determine best fit. Thosedocuments having the smallest inner products are deemed most relevant toa given theme and are identified (block 154). The routine then returns.

FIG. 13 is a table 170 showing the matrix 47 generated by the routine ofFIG. 12. The matrix 47 maps a cluster 171 to documents 172 based on acalculated inner product. Each inner product quantifies similaritiesbetween documents, as represented by a distance. The distance is mappedinto a multi-dimensional concept space for a given document, as measuredby the magnitude of a vector for a given term drawn relative to an angleθ, held constant for the given cluster.

For a set of n documents, the distance d_(cluster) is calculated bytaking the sum of products (inner product) by terms between documentconcept frequency occurrences and cluster concept weightings, using thefollowing equation:

$d_{cluster} = {\sum\limits_{i\rightarrow n}{{doc}_{{term}_{i}} \cdot {cluster}_{{term}_{i}}}}$

where doc_(term) represents the frequency of occurrence for a given termi in the selected document and cluster_(term) represents the weight of agiven cluster for a given term i. The weights of the individual innerproducts are iteratively updated until the clusters settle. The goal isto calculate the minimum distances between as few clusters as possibleuntil the rate of change goes constant. The rate of change can becalculated, for example, by taking the first derivative of the innerproducts over successive iterations.

FIG. 14 is a flow diagram showing the routine 180 for determiningresults for use in the routine of FIG. 5. Duplicate documents 44 areremoved from the results (block 181). The results are re-run (block182), as necessary by repeating the text analysis operations (block183), beginning with creating the corpus graph 49 (block 84 in FIG. 5).After satisfactory results have been obtained (block 182), the routinereturns.

Satisfactory results are shown when a meaningful cluster of documents isfound. Objectively, each document within a given theme will have aninner product falling within a pre-defined variance of other relateddocuments, thereby reflecting a set amount of similarity. The clusteritself represents a larger grouping of document sets based on related,but not identical, themes.

If necessary, the results are re-run (block 182). One reason to re-runthe results set would be to re-center the median value 145 of the corpusgraph 140 (shown in FIG. 11) following the filtering of furtherdocuments 44. The filtering of edge condition concept frequencyoccurrences will cause the curve 144 to be redefined, thereby requiringfurther processing.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A computer-implemented system for identifyingrelevant documents, comprising: a database to store a set of documentseach associated with one or more concepts; and a server comprising acentral processing unit, memory, an input port to receive the documentset, and an output port, wherein the central processing unit isconfigured to: cluster at least a portion of the documents in the setbased on the concepts; and generate a matrix that provides a summary ofthose documents that are most relevant to one such concept bydetermining for each document a measure of similarity between a conceptfrequency occurrence and concept weights of each cluster and populatingthe matrix with the calculated measures of similarity; and identifythose documents associated with a threshold measure of similarity as themost relevant documents to one such concept.
 2. A system according toclaim 1, wherein the central processing unit removes those documentsfrom the matrix with zero values for one or more concept frequencyoccurrences.
 3. A system according to claim 1, wherein the centralprocessing unit calculates the similarity according to the followingequation:$d_{cluster} = {\sum\limits_{i\rightarrow n}{{doc}_{{term}_{i}} \cdot {cluster}_{{term}_{i}}}}$where doc_(term) represents the frequency of occurrence for a given termi in one such document and cluster_(term) represents the weight of agiven cluster for a given term i.
 4. A system according to claim 1,wherein the central processing unit updates the concept weights of theclusters until the clusters settle.
 5. A system according to claim 1,wherein the central processing unit removes duplicate documents from theidentified documents that satisfy the threshold similarity.
 6. A systemaccording to claim 1, wherein the central processing unit identifies thedocuments for clustering by creating a histogram for each document thatmaps a relative frequency of occurrence for at least a portion of termsin that document, creating a corpus graph that maps the concepts for thedocuments in the set based on the terms, defining predefined thresholdsfor the corpus graph, and selecting those documents with terms thatsatisfy the thresholds for clustering.
 7. A system according to claim 6,wherein the central processing unit removes from the set, thosedocuments with terms that fail to satisfy the threshold.
 8. A systemaccording to claim 6, wherein the thresholds are set at 1% to 15% forshorter documents.
 9. A system according to claim 6, wherein the centralprocessing unit sets tighter thresholds for larger documents.
 10. Asystem according to claim 1, wherein the central processing unitextracts terms from the documents in the set and generates a record foreach term.
 11. A computer-implemented method for identifying relevantdocuments, comprising: identifying a set of documents each associatedwith one or more concepts; clustering at least a portion of thedocuments in the set based on the concepts; generating a matrix thatprovides a summary of those documents that are most relevant to one suchconcept, comprising: determining for each document a measure ofsimilarity between a concept frequency occurrence and concept weights ofeach cluster; and populating the matrix with the calculated measures ofsimilarity; and identifying those documents associated with a thresholdmeasure of similarity as the most relevant documents to one suchconcept.
 12. A method according to claim 11, further comprising:removing those documents from the matrix with zero values for one ormore concept frequency occurrences.
 13. A method according to claim 11,further comprising: calculating the similarity according to thefollowing equation:$d_{cluster} = {\sum\limits_{i\rightarrow n}{{doc}_{{term}_{i}} \cdot {cluster}_{{term}_{i}}}}$where doc_(term) represents the frequency of occurrence for a given termi in one such document and cluster_(term) represents the weight of agiven cluster for a given term i.
 14. A method according to claim 11,further comprising: updating the concept weights of the clusters untilthe clusters settle.
 15. A method according to claim 11, furthercomprising: removing duplicate documents from the identified documentsthat satisfy the threshold similarity.
 16. A method according to claim11, further comprising: identifying the documents for clustering,comprising: creating a histogram for each document that maps a relativefrequency of occurrence for at least a portion of terms in thatdocument; creating a corpus graph that maps the concepts for thedocuments in the set based on the terms; defining predefined thresholdsfor the corpus graph; selecting those documents with terms that satisfythe thresholds for clustering.
 17. A method according to claim 16,further comprising: removing from the set, those documents with termsthat fail to satisfy the threshold.
 18. A method according to claim 16,wherein the thresholds are set at 1% to 15% for shorter documents.
 19. Amethod according to claim 16, further comprising: setting tighterthresholds for larger documents.
 20. A method according to claim 11,further comprising: extracting terms from the documents in the set; andgenerating a record for each term.